We plug ADC into the backbones of frequently used HPE models, including the family of SimpleBaseline [1]} and HRNet [2]}. Their backbones are built up with residual blocks [3]}. As shown in Figure REF , we replace one ordinary convolution layer in the original residual block by ADC. The weights of the last layer in the dilated-rates regression module are initialized as zeros and its bias are initialized as ones. Thus, the generated dilation rates in ADC are initialized are ones. The dilation groups \(g\)  are set as \(g=C_{in}\) , in which case each group contains only one channel. Thus every channel can exploit context information at different scales, and ADC could fuse as much richer multi-scale information as it can. We also experimentally demonstrate that the performance is positively correlated to \(g\)  in Sec REF .
